Artificial Intelligence (AI) in Cybersecurity, Part 28: Using ADStrike to Compromise Active Directory
Welcome back, aspiring cyberwarriors!
Our article on ADScan and ADPulse was popular. Those tools didn’t need the AI to work, they were basically using python libraries to do all the heavy work of analyzing the Active Directory setup. Today we will look at ADStike, which is somewhat similar but uses AI to arrange the attacks. AI here is completely optional. It analyzes the results of each step and decides what tool to run and what information to find. Models that support tool calls can be used for it, like Qwen 2.5-coder 7b.
ADStike has all the common AD attacks and different levels of OPSEC modes with 52 attack modules integrated. We will look at the workflow it has and use a local AI model to direct the pentest.
Setting Up
The repository has the installation process laid out. Let’s use it.
kali > git clone https://github.com/capture0x/AdStrike.git && cd AdStrike && chmod +x install.sh run.sh && bash install.sh && source venv/bin/activate && bash run.sh

Usually it takes 5 minutes to set things up. Then you will be prompted to fill out the information about the domain. We are using our lab here with a regular account that doesn’t have any privileges in the domain to make it fair.

When it has all the needed information, it will be saved in a config file. You will then see the main menu with options. Each phase has separate steps that can run individually.

We will go with step 53, which is AI. The tool already knows what local Ollama models we have installed and lets us choose if there are some. For this test we kept OPSEC level default.

Once you select the needed options, the scan will begin.
Pentesting Active Directory
The AI orchestrator will start from the first phase (Recon) and select only the appropriate phases and steps for your Active Directory setsups. All environments are different, some attacks will simply be impossible due to infrastructure limitations.

As it progresses we start seeing the output on the screen from the commands it runs.
Basically, it does all you would do but quicker. Below you can see that LAPS were empty, because they were not set up in this domain. You might want to recommend your client fix it, if you encounter the same results. LAPS help a lot in preventing lateral movement, since the Administrator password will never be the same across your machines.

Then Kerberoasting follows. We did have a few roastable accounts. The compromise here will depend on your wordlist.

Since it’s our lab, we knew the password of that user.

Another attack you may see is Timeroasting. Here it uses an impacket script to extract computer hashes. While those are basically impossible to brute force, some computers are vulnerable to Pre2k attack, where the password of the computer matches the DNS name.

Here is more on Pre2k. Look at the NetExec commands. It’s passing the computer name and the password to the DC to find a valid account. These machines can have certain privileges, just like users do. All the privileges can be found in BloodHound. By the way, ADStrike will run SharpHound collection and analyze the data with AI to find paths and worthy accounts.

Finally, after all the necessary steps, you will have a nice report generated in different formats.

Let’s open the HTML version and see what it looks like.

Scrolling down we see the MITRE ATT&CK Coverage

Then we have all the findings and their details.

Summary
ADStrike looks very promising and easy to work with. It has 52 attack modules that are orchestrated with AI. AI here is completely optional and things should work without it as well. If you prefer not to let AI execute anything in your environment, you can use it for planning only or leave it alone to pwn your lab.
Join our AI for Cybersecurity training to learn more! During the training, we’ll show you different ways of using AI in cybersecurity, set up local models and solve labs. The field is evolving rapidly and the sooner you learn things, the greater the advantage you’ll have.
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